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Instructors:

Qingkai Kong is a PhD student in Geophysics / Seismology. Qingkai's research interests are in earthquake detection, machine learning application in seismology. He is currently working on MyShake project to build a smartphone seismic network that utilizing the sensors inside smartphones to detect the earthquakes. You can find more details about Qingkai here.

This workshop introduces Artificial Neural Networks (ANNs), a group of popular machine learning algorithms. No prior knowledge is required, though previous experience with other machine learning algorithms would be helpful. The workshop will be divided into 3 parts:

A brief history of ANNs and an explanation of the intuition behind them. This part aims to give the audience a conceptual understanding with few mathematical barriers, and no programming requirements.

Step-by-step construction of a very basic ANN. Although the code will be written in Python, it will be intuitive enough for programmers of other languages to follow along.

Using the popular Python library scikit-learn, an ANN will be implemented on a classification problem. High-level libraries reduce the work for a researcher implementing ANN down to tuning a set of parameters, which will be explained in this part.

Prior knowledge: D-Lab's Python for Everything or R Fundamentals and an interest in machine learning.

Technology requirement: To follow along in parts 2 and 3, it is suggested to install Python via Anaconda. Instructions can be found here.